一种用于放射治疗反应评估的新型定量多参数磁共振序列的评估

Evaluation of a Novel Quantitative Multiparametric MR Sequence for Radiation Therapy Treatment Response Assessment.

作者信息

Yan Yuhao, Bayliss R Adam, Wiesinger Florian, Rodriguez Jose de Arcos, Burr Adam R, Baschnagel Andrew M, Morris Brett A, Glide-Hurst Carri K

机构信息

Department of Human Oncology, University of Wisconsin-Madison, Madison, WI.

Department of Medical Physics, University of Wisconsin-Madison, Madison, WI.

出版信息

ArXiv. 2025 Mar 28:arXiv:2503.22640v1.

PMID:40196149

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11975303/

Abstract

BACKGROUND

Multi-parametric MRI has shown great promise to rapidly derive multiple quantitative imaging biomarkers for treatment response assessment.

PURPOSE

To evaluate a novel Deep-Learning-enhanced MUlti-PArametric MR sequence (DL-MUPA) for treatment response assessment for brain metastases patients treated with stereotactic radiosurgery (SRS) and head-and-neck (HnN) cancer patients undergoing conventionally fractionation adaptive radiation therapy.

METHODS

DL-MUPA derives quantitative T1 and T2 relaxation time maps from a single 4-6-minute scan denoised via DL method using least-squares dictionary fitting. Longitudinal phantom benchmarking was performed on a NIST-ISMRM phantom over one year. In patients, longitudinal DL-MUPA data were acquired on a 1.5T MR-simulator, including pre-treatment (PreTx) and every ~3 months after SRS (PostTx) in brain, and PreTx, mid-treatment and 3 months PostTx in HnN. Delta analysis was performed calculating changes of mean T1 and T2 values within gross tumor volumes (GTVs), residual disease (RD, HnN), parotids, and submandibular glands (HnN) for treatment response assessment. Uninvolved normal tissues (normal appearing white matter in brain, masseter in HnN) were evaluated to quantify within-subject repeatability.

RESULTS

Phantom benchmarking revealed excellent inter-session repeatability (coefficient of variance <0.9% for T1, <6.6% for T2), suggesting reliability for longitudinal studies once systematic biases are adjusted. Uninvolved normal tissue suggested acceptable within-subject repeatability (brain |ΔT1|<36ms/5.0%, |ΔT2|<2ms/5.0%, HnN |ΔT1|<69ms/7.0%, |ΔT2|<4ms/17.8% due to low T2). In brain, remarkable changes were noted in resolved metastasis (4-month PostTx ΔT1=155ms/13.7%) and necrotic settings (ΔT1=214-502ms/17.6-39.7%, ΔT2=7-41ms/8.7-41.4%, 6-month to 3-month PostTx). In HnN, two base of tongue tumors exhibited T2 enhancement (PostTx GTV ΔT2>7ms/12.8%, RD ΔT2>10ms/18.1%). A case with nodal disease resolved PostTx (GTV ΔT1=-541ms/-39.5%, ΔT2=-24ms/-32.7%, RD ΔT1=-400ms/-29.2%, ΔT2=-25ms/-35.3%). Enhancement was found in involved parotids (PostTx ΔT1>82ms/12.4%, ΔT2>6ms/13.4%) and submandibular glands (PostTx ΔT1>135ms/14.6%, ΔT2>17ms/34.5%) while the uninvolved organs remained stable.

CONCLUSIONS

Preliminary results suggest promise of DL-MUPA for treatment response assessment and highlight potential endpoints for functional sparing.

摘要

背景

多参数磁共振成像在快速获取多种定量成像生物标志物以评估治疗反应方面显示出巨大潜力。

目的

评估一种新型的深度学习增强多参数磁共振序列（DL-MUPA），用于评估接受立体定向放射治疗（SRS）的脑转移瘤患者以及接受常规分割自适应放射治疗的头颈部（HnN）癌症患者的治疗反应。

方法

DL-MUPA通过使用最小二乘字典拟合的深度学习方法对单次4 - 6分钟扫描进行去噪，从而获得定量的T1和T2弛豫时间图。在一个NIST-ISMRM体模上进行了为期一年的纵向体模基准测试。在患者中，在1.5T MR模拟机上采集纵向DL-MUPA数据，包括脑转移瘤患者在SRS治疗前（PreTx）以及治疗后每3个月左右（PostTx）的数据，HnN癌症患者在治疗前、治疗中期和治疗后3个月的数据。进行增量分析，计算大体肿瘤体积（GTV）、残留病灶（RD，HnN）、腮腺和颌下腺（HnN）内平均T1和T2值的变化，以评估治疗反应。对未受累的正常组织（脑内正常外观的白质、HnN内的咬肌）进行评估，以量化受试者内的重复性。

结果

体模基准测试显示出极好的组间重复性（T1的变异系数<0.9%，T2的变异系数<6.6%），表明一旦调整系统偏差，纵向研究具有可靠性。未受累的正常组织显示出可接受的受试者内重复性（脑|ΔT1|<36ms/5.0%，|ΔT2|<2ms/5.0%，HnN由于T2值较低，|ΔT1|<69ms/7.0%，|ΔT2|<4ms/17.8%）。在脑内，在消退的转移瘤（治疗后4个月ΔT1 = 155ms/13.7%）和坏死区域（ΔT1 = 214 - 502ms/17.6 - 39.7%，ΔT2 = 7 - 41ms/8.7 - 41.4%，治疗后6个月至3个月）中观察到显著变化。在HnN中，两个舌根肿瘤表现出T2增强（治疗后GTV ΔT2>7ms/12.8%，RD ΔT2>10ms/18.1%）。1例淋巴结疾病在治疗后消退（GTV ΔT1 = -541ms/-39.5%，ΔT2 = -24ms/-32.7%，RD ΔT1 = -400ms/-29.2%，ΔT2 = -25ms/-35.3%）。在受累的腮腺（治疗后ΔT1>82ms/12.4%，ΔT2>6ms/13.4%）和颌下腺（治疗后ΔT1>135ms/14.6%，ΔT2>17ms/34.5%）中发现增强，而未受累的器官保持稳定。